tigeR

tigeR is an R package designed for the analysis of gene expression in tumor immunotherapy.

1.Introduction

  • 1060 samples with immunotherapy clinical information from 11 melanoma datasets, including 3 lung cancer datasets, 2 kidney cancer datasets, 1 gastric cancer dataset, 1 low-grade glioma dataset, 1 glioblastoma dataset and 1 Head and Neck Squamous data set (all organized into R language ‘SummarizedExperiment’ objects).

  • 23 immunotherapy response related biomarkers from literature, multiple methods for analysis and visualization.

  • 10 open source tumor microenvironment deconvolution methods including CIBERSORT, TIMER, ESTIMATE, IPS, xCell, EPIC, ConsensusTME, ABIS, quanTIseq, and MCPCounter. Several downstream method for analysis and visualization.

  • 6 machine learning method for multi-modal prediction model construction and testing.

Overall design of tigeR

2.Installation

packages <- c("BiocManager", "devtools", "ggplot2", "pROC")
for (package in packages) {
  if (!require(package, character.only = TRUE)) {
    install.packages(package)
  }
}
devtools::install_github("YuLab-SMU/tigeR")

3.Quick Start

The workflow of tigeR is below, see more details in tigeR documentation.

Workflow of tigeR

4.TIGER web server

http://tiger.canceromics.org/#/